Update README with gifs

Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>

docs/README_2d3dtracking.md

@@ -18,6 +18,8 @@ where
 
 * ```<calibration-file>``` is the camera calibration file (opencv format). It is important that the file contains entry "camera_matrix" with sub-entry "rows", "cols", "data". If you do not want to pass the calibration file, pass "NULL" instead.
 
+![demo](https://user-images.githubusercontent.com/11939259/126784875-c4285497-d369-424f-abda-58274cd747ac.gif)
+
 ## Object Detection and Tracking
 
 To run the 3D object detection & tracking demo follow these steps (example with CenterTrack based on DLA34):
@@ -37,6 +39,7 @@ where
 * ```<calibration-file>``` is the camera calibration file (opencv format). It is important that the file contains entry "camera_matrix" with sub-entry "rows", "cols", "data". If you do not want to pass the calibration file, pass "NULL" instead.
 *  ```<2D/3D-flag>``` if set to 0 the demo will be in the 2D mode, while if set to 1 the demo will be in the 3D mode (Default is 1 - 3D mode).
 
+![demo](https://user-images.githubusercontent.com/11939259/126784878-513fa9e8-864a-4c24-b4bd-199737184708.gif)
 
 ## FPS Results
 

docs/README_seg.md

@@ -29,7 +29,7 @@ where
 NB) By default it is used FP32 inference
 NB) The batching is not used to work on more streams, rather to work on more tiles of the same image. Shelfnet never resized the input image, therefore for images greater than 1024x1024 tiles of 1024x1024 are given in input to the network in batch. 
 
-![gif](output.gif "Results on yolo_test.mp4")  
+![demo](https://user-images.githubusercontent.com/11939259/126784236-38d24fc3-02df-4514-81c4-497e87e40b65.gif "Results on yolo_test.mp4")  
 
 For other demo videos refer to [this playlist](https://www.youtube.com/playlist?list=PLv0nEQYDD45y5EdSiywwCGPBmJVUzIWwe).